Detail publikace

Text Augmentation for Language Models in High Error Recognition Scenario

BENEŠ, K. BURGET, L.

Originální název

Text Augmentation for Language Models in High Error Recognition Scenario

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this paper, we explore several data augmentation strategies for training of language models for speech recognition. We compare augmentation based on global error statistics with one based on unigram statistics of ASR errors and with labelsmoothing and its sampled variant. Additionally, we investigate the stability and the predictive power of perplexity estimated on augmented data. Despite being trivial, augmentation driven by global substitution, deletion and insertion rates achieves the best rescoring results. On the other hand, even though the associated perplexity measure is stable, it gives no better prediction of the final error rate than the vanilla one. Our best augmentation scheme increases the WER improvement from second-pass rescoring from 1.1% to 1.9% absolute on the CHiMe-6 challenge.

Klíčová slova

data augmentation, error simulation, language modeling, automatic speech recognition

Autoři

BENEŠ, K.; BURGET, L.

Vydáno

30. 8. 2021

Nakladatel

International Speech Communication Association

Místo

Brno

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Ročník

2021

Číslo

8

Stát

Francouzská republika

Strany od

1872

Strany do

1876

Strany počet

5

URL

https://www.isca-speech.org/archive/interspeech_2021/benes21_interspeech.html

BibTex

@inproceedings{BUT175841,
  author="Karel {Beneš} and Lukáš {Burget}",
  title="Text Augmentation for Language Models in High Error Recognition Scenario",
  booktitle="Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
  year="2021",
  journal="Proceedings of Interspeech",
  volume="2021",
  number="8",
  pages="1872--1876",
  publisher="International Speech Communication Association",
  address="Brno",
  doi="10.21437/Interspeech.2021-627",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/interspeech_2021/benes21_interspeech.html"
}